Week 10 Hour 1. Shapiro-Wilks Test (from last time) Cross-Validation. Week 10 Hour 2 Missing Data. Stat 302 Notes. Week 10, Hour 2, Page 1 / 32
|
|
- Mercy Moody
- 5 years ago
- Views:
Transcription
1 Week 10 Hour 1 Shapiro-Wilks Test (from last time) Cross-Validation Week 10 Hour 2 Missing Data Stat 302 Notes. Week 10, Hour 2, Page 1 / 32
2 Cross-Validation in the Wild It s often more important to describe the patterns in the data in a way that also applies to new, similar situations. That s why the standard way of comparing different models from different sources is Cross-Validation. There are ongoing competitions at kaggle.com where the training set is given out the public in full, and the test set is given out, but with the response variable blanked out. Stat 302 Notes. Week 10, Hour 2, Page 2 / 32
3 The Netflix Prize In , Netflix held a competition with a million dollar prize to find a better model to predict how users would rate movies. They weren t interested in a model that simply fitted the ratings that were already given. They wanted one that could be applied to predict ratings for movies their users had not yet seen. Stat 302 Notes. Week 10, Hour 2, Page 3 / 32
4 For example: Assume user John gave both movies Braveheart and Mad Max a high rating. User Marsha gave Braveheart a low rating. We can use the above information predict that she won t like Mad Max either. Netflix wants to present recommendations that their users will like, so that they stay. Since the problem of interest was to predict new scores, not existing ones, cross-validation was key. Stat 302 Notes. Week 10, Hour 2, Page 4 / 32
5 The training data set had 100,480,507 ratings on 17,770 movies from 480,159 different users; the test set had only 2,817,131 ratings. The data looked like this: Stat 302 Notes. Week 10, Hour 2, Page 5 / 32
6 The winning team improved the prediction error by 10% over Netflix s original system. (The square root of the PRESS statistic) Stat 302 Notes. Week 10, Hour 2, Page 6 / 32
7 solve one problem, and another takes its place. Stat 302 Notes. Week 10, Hour 2, Page 7 / 32
8 Missing Data Missing, or incomplete, data has several definitions. For the sake of this class, we will treat it to be: Any occurrence where data for a variable has not been recorded for some observation is considered missing from that observation. In the Netflix example in cross-validation, the responses in the test set were missing data. Missing data shows in R as NA for Not Applicable. Stat 302 Notes. Week 10, Hour 2, Page 8 / 32
9 Why do we care about missing data? The default approach is to ignore any observation in a model that has any model variable missing. For example, in the birth rate model using the gapminder data, any country that didn t have a recorded value for GDPpercap is not included in any model that uses GDPpercap. Stat 302 Notes. Week 10, Hour 2, Page 9 / 32
10 Ignoring observations with missing data is bad for 3 reasons. 1. It s wasteful. If every other relevant variable is recorded for a country, none of that information is used in the model. In the following images, red squares represent missing data and blue squares unused data. A small portion ( about 8%) of the data is missing, but a model using all 11 explanatory variables is to use 8 of the 10 (80%) of the cases. A relatively small amount of missing data can have a big impact on your sample sizes, especially when many variables are used. Stat 302 Notes. Week 10, Hour 2, Page 10 / 32
11 Red squares are missing data. White squares are filled data. Stat 302 Notes. Week 10, Hour 2, Page 11 / 32
12 Stat 302 Notes. Week 10, Hour 2, Page 12 / 32 Blue squares are unused data.
13 Ignoring observations with missing data is bad for 3 reasons. 2. It creates inconsistency. Countries are only excluded from the model if one or more of the model s variables are missing for that country. If one model uses GDPpercap, and another doesn t, but some countries have missing GDP data, then how are the two supposed to be compared? Stat 302 Notes. Week 10, Hour 2, Page 13 / 32
14 2. It creates inconsistency. (Continued) Methods based on likelihood, such as the AIC and BIC, need the same set of observations between every model in order to make a viable comparison. In some cases where the AIC is used in an automated process, that process will fail to run when there is missing data. In the following image, blue is unused data, but only when the explanatory variables x2 and x6 aren t used. Stat 302 Notes. Week 10, Hour 2, Page 14 / 32
15 Blue squares are unused data when only x2 and x6 are unused. Stat 302 Notes. Week 10, Hour 2, Page 15 / 32
16 Ignoring observations with missing data is bad for 3 reasons. 3. It creates bias. What countries are most likely to have missing GDP per capita data, or missing data for most other variables? Countries with low income and a poorly developed vital statistics department? Countries with high corruption that are prone to misreporting vital data? The trends we see may only apply to developed countries. Stat 302 Notes. Week 10, Hour 2, Page 16 / 32
17 Missing data is a problem that can t be solved by ignoring it. Stat 302 Notes. Week 10, Hour 2, Page 17 / 32
18 Missing data comes in three classes*. 1. MCAR: Missing Completely At Random 2. MAR: Missing At Random 3. NMAR: Not Missing At Random These classes represent both the inherit reasons for data to be missing, and the range of things we can do about it to preserve validity. *The MAR class of data is further split in your reading for Assignment 4. Stat 302 Notes. Week 10, Hour 2, Page 18 / 32
19 MCAR Missing Completely At Random If every value for a variable has the same chance of not being recorded, missing data for this variable is considered MCAR. Example: Imagine tracking the number of cars at an intersection over time using a webcam. But the Wi-Fi on your laptop fails occasionally, and you cannot record cars during the outage. The missing car counts are MCAR. Stat 302 Notes. Week 10, Hour 2, Page 19 / 32
20 The fact that they are missing has nothing to do with the cars. Their missingness also has nothing to do with the weather, time of day, or any other variable related to the cars. In short, not having that data just means you have a smaller sample. MCAR data is also called data with ignorable missingness because the parameter estimates you get from a model should be the same whether the data is imputed or the observations removed. Stat 302 Notes. Week 10, Hour 2, Page 20 / 32
21 MAR: Missing At Random If the chance that a value is missing can be determined entirely by other variables in the dataset, then the data is missing at random. Returning to the car counting example. Say the webcam is known to shut down every night from 1am to 5am to save power, or because it s too dark. These missing car counts are MAR. Stat 302 Notes. Week 10, Hour 2, Page 21 / 32
22 A pollster can assume that phone calls made between 9am and 5pm have a small chance of being answered unless the respondent is a stay-at-home parent. In this case, the chance that the responses for anything in the content of the call being missing can be explained by the time of call, and the demographics of the group or person receiving this call. This information would be given by the address, etc. In other words, if you know WHY a certain value is missing or not, then you can assume that the missingness in your data is either MAR or MCAR. Stat 302 Notes. Week 10, Hour 2, Page 22 / 32
23 There are lots of mechanisms that can cause data to be missing at random. Some could even be intentional. When pollsters survey people, they often skip questions that they know are irrelevant based on previous answers. Someone who reports their biological sex as male isn t going to have relevant answers to questions about their pregnancies. Someone who has never heard of a politician will not have relevant information about their opinion of them. Stat 302 Notes. Week 10, Hour 2, Page 23 / 32
24 NMAR: Not Missing At Random Of the three types of missingness, this is by far the worst. If data is NMAR, the chance that any value for the given variable is missing depends on data which is itself missing. The Census encounters NMAR data with it uses households to measure key population statistics. People who do not live in permanent homes are much more likely to have missing data in a census (or most order surveys) because they less likely to be found by pollsters. Stat 302 Notes. Week 10, Hour 2, Page 24 / 32
25 This sort of non-random missingness can lead to biases such as underestimating mental or physical disease prevalence in a community. The worst part is that the biases can t be corrected for, because we don t what data is missing, and we can t explain why any given value is missing from the We also can t reliably fill in values in the missing data with imputation because we cannot estimate what reasonable replacements should look like. Stat 302 Notes. Week 10, Hour 2, Page 25 / 32
26 Imputation Imputation is the act of filling in missing data. Missing data be filled with predefined values (e.g. 0) It can be filled with predictions of what the values should be. It can even be filled with more than one value per missing data point. Details of imputation will be given next day. Stat 302 Notes. Week 10, Hour 2, Page 26 / 32
27 Next day: Imputation. (end of Midterm 2 material) Pepsi Challenge Source: Malcolm Gladwell The Power of Thinking without Thinking, cited in Stat 302 Notes. Week 10, Hour 2, Page 27 / 32
28 Optional discussion: The circular argument and crossvalidation. A hypothesis test asks the question how likely is this observed pattern/difference by random chance alone. This works great when we have a specific relationship of difference to example from the very start. Example: A basic clinical trial Does this drug show a difference in the intended outcome, compared to a placebo, in the general population of sufferers? Stat 302 Notes. Week 10, Hour 2, Page 28 / 32
29 Answer: T-Test Sometimes the question isn t restrictive to fit into a single hypothesis test. Example: An open clinical trial - Does this drug show a difference in the intended outcome, compared to a placebo, in a subgroup of the population of sufferers? To answer an open question like this, we may start with a t-test using entire sample. After that, it is typical to examine the data and look for patterns (e.g. by doing a multiple regression, or a model selection). Stat 302 Notes. Week 10, Hour 2, Page 29 / 32
30 When we look through a dataset for patterns, we are conducting exploratory data analysis. After exploratory data analysis is done, hypothesis tests follow. and here we find a circular argument. In a sufficiently complex dataset, there could be hundreds of possible hypotheses to test. When exploring the data, you are going to find some of the most promising ones. Then you test to see if those ones are random noise. Stat 302 Notes. Week 10, Hour 2, Page 30 / 32
31 Are the ones you decide to test, (e.g. with an multi-way ANOVA, or a multiple regression) going to be a fair representation of the possibilities? Of course not. We have already addressed a similar problem with multiple comparisons. Could it work here too? Our multiple comparison options: 1. P-value adjustments (e.g. Bonferroni correction) 2. Post-hoc tests (e.g. Tukey s Honestly Significant Difference) Stat 302 Notes. Week 10, Hour 2, Page 31 / 32
32 For the Bonferroni and for the Tukey HSD (and any other p- value adjustment or post-hoc test), we need to know the number of hypotheses are being tested. How many hypotheses are there when exploring a dataset? Do terms not included in the multiple regression count? Do modifications of terms (transforms, polynomials, interactions) that weren t considered count? It s unclear, and any answer would be arbitrary. Stat 302 Notes. Week 10, Hour 2, Page 32 / 32
Stepwise method Modern Model Selection Methods Quantile-Quantile plot and tests for normality
Week 9 Hour 3 Stepwise method Modern Model Selection Methods Quantile-Quantile plot and tests for normality Stat 302 Notes. Week 9, Hour 3, Page 1 / 39 Stepwise Now that we've introduced interactions,
More informationProblem 1) Match the terms to their definitions. Every term is used exactly once. (In the real midterm, there are fewer terms).
Problem 1) Match the terms to their definitions. Every term is used exactly once. (In the real midterm, there are fewer terms). 1. Bayesian Information Criterion 2. Cross-Validation 3. Robust 4. Imputation
More informationWeek 8 Hour 1: More on polynomial fits. The AIC. Hour 2: Dummy Variables what are they? An NHL Example. Hour 3: Interactions. The stepwise method.
Week 8 Hour 1: More on polynomial fits. The AIC Hour 2: Dummy Variables what are they? An NHL Example Hour 3: Interactions. The stepwise method. Stat 302 Notes. Week 8, Hour 1, Page 1 / 34 Human growth
More informationStat 13, Intro. to Statistical Methods for the Life and Health Sciences.
Stat 13, Intro. to Statistical Methods for the Life and Health Sciences. 0. SEs for percentages when testing and for CIs. 1. More about SEs and confidence intervals. 2. Clinton versus Obama and the Bradley
More informationValidity and reliability of measurements
Validity and reliability of measurements 2 Validity and reliability of measurements 4 5 Components in a dataset Why bother (examples from research) What is reliability? What is validity? How should I treat
More informationValidity and reliability of measurements
Validity and reliability of measurements 2 3 Request: Intention to treat Intention to treat and per protocol dealing with cross-overs (ref Hulley 2013) For example: Patients who did not take/get the medication
More informationSelected Topics in Biostatistics Seminar Series. Missing Data. Sponsored by: Center For Clinical Investigation and Cleveland CTSC
Selected Topics in Biostatistics Seminar Series Missing Data Sponsored by: Center For Clinical Investigation and Cleveland CTSC Brian Schmotzer, MS Biostatistician, CCI Statistical Sciences Core brian.schmotzer@case.edu
More informationMath 140 Introductory Statistics
Math 140 Introductory Statistics Professor Silvia Fernández Sample surveys and experiments Most of what we ve done so far is data exploration ways to uncover, display, and describe patterns in data. Unfortunately,
More informationDesigning and Analyzing RCTs. David L. Streiner, Ph.D.
Designing and Analyzing RCTs David L. Streiner, Ph.D. Emeritus Professor, Department of Psychiatry & Behavioural Neurosciences, McMaster University Emeritus Professor, Department of Clinical Epidemiology
More informationAnalysis of TB prevalence surveys
Workshop and training course on TB prevalence surveys with a focus on field operations Analysis of TB prevalence surveys Day 8 Thursday, 4 August 2011 Phnom Penh Babis Sismanidis with acknowledgements
More informationSample size calculation a quick guide. Ronán Conroy
Sample size calculation a quick guide Thursday 28 October 2004 Ronán Conroy rconroy@rcsi.ie How to use this guide This guide has sample size ready-reckoners for a number of common research designs. Each
More informationStudy Buddies. More Info, Please. By Meredith Matthews. Teens talk about being part of medical trials or not.
Study Buddies By Meredith Matthews Teens talk about being part of medical trials or not. The next time you pop a pill for a backache or a cold, check out the label. It may say the drug is safe and effective
More informationRisk Aversion in Games of Chance
Risk Aversion in Games of Chance Imagine the following scenario: Someone asks you to play a game and you are given $5,000 to begin. A ball is drawn from a bin containing 39 balls each numbered 1-39 and
More informationComparing 3 Means- ANOVA
Comparing 3 Means- ANOVA Evaluation Methods & Statistics- Lecture 7 Dr Benjamin Cowan Research Example- Theory of Planned Behaviour Ajzen & Fishbein (1981) One of the most prominent models of behaviour
More informationPaul Figueroa. Washington Municipal Clerks Association ANNUAL CONFERENCE. Workplace Bullying: Solutions and Prevention. for
Workplace Bullying: Solutions and Prevention for Washington Municipal Clerks Association ANNUAL CONFERENCE Paul@PeaceEnforcement.com 206-650-5364 Peace Enforcement LLC Bullying described: Why people do
More informationAdvanced Handling of Missing Data
Advanced Handling of Missing Data One-day Workshop Nicole Janz ssrmcta@hermes.cam.ac.uk 2 Goals Discuss types of missingness Know advantages & disadvantages of missing data methods Learn multiple imputation
More informationThree Questions You MUST Ask Before Hiring a Sleep Consultant
Three Questions You MUST Ask Before Hiring a Sleep Consultant Provided by Jennifer Schindele Certified Sleep Sense Consultant 2015 Sleep Sense Publishing Inc. BABY NOT SLEEPING THROUGH THE NIGHT? You re
More informationSheila Barron Statistics Outreach Center 2/8/2011
Sheila Barron Statistics Outreach Center 2/8/2011 What is Power? When conducting a research study using a statistical hypothesis test, power is the probability of getting statistical significance when
More informationModule 14: Missing Data Concepts
Module 14: Missing Data Concepts Jonathan Bartlett & James Carpenter London School of Hygiene & Tropical Medicine Supported by ESRC grant RES 189-25-0103 and MRC grant G0900724 Pre-requisites Module 3
More informationNEXT. Top 12 Myths about OEE
NEXT Top 12 Myths about OEE TOP 12 MYTHS ABOUT OEE 2/10 Thousands of pages have been written about OEE (Overall Equipment Effectiveness). Some of it is clear and useful; much of it is not. Personally,
More informationMeeting a Kid with Autism
What s up with Nick? When school started, we had a new kid named Nick. He seemed a little different. My friends and I wondered, What's up with Nick? Turns out, Nick has autism. What is Autism This year,
More informationOur goal in this section is to explain a few more concepts about experiments. Don t be concerned with the details.
Our goal in this section is to explain a few more concepts about experiments. Don t be concerned with the details. 1 We already mentioned an example with two explanatory variables or factors the case of
More informationChapter 1. Dysfunctional Behavioral Cycles
Chapter 1. Dysfunctional Behavioral Cycles For most people, the things they do their behavior are predictable. We can pretty much guess what someone is going to do in a similar situation in the future
More informationDesigning Psychology Experiments: Data Analysis and Presentation
Data Analysis and Presentation Review of Chapter 4: Designing Experiments Develop Hypothesis (or Hypotheses) from Theory Independent Variable(s) and Dependent Variable(s) Operational Definitions of each
More informationThe Wellbeing Course. Resource: Mental Skills. The Wellbeing Course was written by Professor Nick Titov and Dr Blake Dear
The Wellbeing Course Resource: Mental Skills The Wellbeing Course was written by Professor Nick Titov and Dr Blake Dear About Mental Skills This resource introduces three mental skills which people find
More informationHelp! Statistics! Missing data. An introduction
Help! Statistics! Missing data. An introduction Sacha la Bastide-van Gemert Medical Statistics and Decision Making Department of Epidemiology UMCG Help! Statistics! Lunch time lectures What? Frequently
More informationStatistical data preparation: management of missing values and outliers
KJA Korean Journal of Anesthesiology Statistical Round pissn 2005-6419 eissn 2005-7563 Statistical data preparation: management of missing values and outliers Sang Kyu Kwak 1 and Jong Hae Kim 2 Departments
More informationMeasuring the User Experience
Measuring the User Experience Collecting, Analyzing, and Presenting Usability Metrics Chapter 2 Background Tom Tullis and Bill Albert Morgan Kaufmann, 2008 ISBN 978-0123735584 Introduction Purpose Provide
More informationMAT Mathematics in Today's World
MAT 1000 Mathematics in Today's World Last Time 1. What does a sample tell us about the population? 2. Practical problems in sample surveys. Last Time Parameter: Number that describes a population Statistic:
More information2015 Survey on Prescription Drugs
2015 Survey on Prescription Drugs AARP Research January 26, 2016 (For media inquiries, contact Gregory Phillips at 202-434-2544 or gphillips@aarp.org) https://doi.org/10.26419/res.00122.001 Objectives
More informationUNIT. Experiments and the Common Cold. Biology. Unit Description. Unit Requirements
UNIT Biology Experiments and the Common Cold Unit Description Content: This course is designed to familiarize the student with concepts in biology and biological research. Skills: Main Ideas and Supporting
More informationCommentary on The Erotetic Theory of Attention by Philipp Koralus. Sebastian Watzl
Commentary on The Erotetic Theory of Attention by Philipp Koralus A. Introduction Sebastian Watzl The study of visual search is one of the experimental paradigms for the study of attention. Visual search
More informationAppendix: Instructions for Treatment Index B (Human Opponents, With Recommendations)
Appendix: Instructions for Treatment Index B (Human Opponents, With Recommendations) This is an experiment in the economics of strategic decision making. Various agencies have provided funds for this research.
More informationSubliminal Messages: How Do They Work?
Subliminal Messages: How Do They Work? You ve probably heard of subliminal messages. There are lots of urban myths about how companies and advertisers use these kinds of messages to persuade customers
More informationP. 266 #9, 11. p. 289 # 4, 6 11, 14, 17
P. 266 #9, 11 9. Election. a) Answers will vary. A component is one voter voting. An outcome is a vote for our candidate. Using two random digits, 00-99, let 01-55 represent a vote for your candidate,
More informationBiostatistics 3. Developed by Pfizer. March 2018
BROUGHT TO YOU BY Biostatistics 3 Developed by Pfizer March 2018 This learning module is intended for UK healthcare professionals only. Job bag: PP-GEP-GBR-0986 Date of preparation March 2018. Agenda I.
More informationAnalysis of Variance (ANOVA)
Research Methods and Ethics in Psychology Week 4 Analysis of Variance (ANOVA) One Way Independent Groups ANOVA Brief revision of some important concepts To introduce the concept of familywise error rate.
More informationQuantitative Evaluation
Quantitative Evaluation Research Questions Quantitative Data Controlled Studies Experimental Methods Role of Statistics Quantitative Evaluation What is experimental design? What is an experimental hypothesis?
More informationNEW JERSEY RESIDENTS DON T KNOW OR LIKE MUCH ABOUT COMMON CORE
For immediate release Monday, March 9, 2015 9 pages Contact Dan Cassino 973.896.7072 dcassino@fdu.edu @dancassino NEW JERSEY RESIDENTS DON T KNOW OR LIKE MUCH ABOUT COMMON CORE With PARCC (Partnership
More informationExam 2 PS 306, Spring 2004
Exam 2 PS 306, Spring 2004 1. Briefly define the term confound. Then, using a very explicit example of practice effects (maybe even with numbers?), illustrate why conducting a repeated measures experiment
More informationSexual Feelings. Having sexual feelings is not a choice, but what you do with your feelings is a choice. Let s take a look at this poster.
Sexual Feelings It may be your first instinct to deny that your child will ever have sexual feelings. You may even do everything in your power to suppress those feelings if they do surface. You won t succeed
More informationHelping Your Asperger s Adult-Child to Eliminate Thinking Errors
Helping Your Asperger s Adult-Child to Eliminate Thinking Errors Many people with Asperger s (AS) and High-Functioning Autism (HFA) experience thinking errors, largely due to a phenomenon called mind-blindness.
More informationREVIEW FOR THE PREVIOUS LECTURE
Slide 2-1 Calculator: The same calculator policies as for the ACT hold for STT 315: http://www.actstudent.org/faq/answers/calculator.html. It is highly recommended that you have a TI-84, as this is the
More informationChapter 11. Experimental Design: One-Way Independent Samples Design
11-1 Chapter 11. Experimental Design: One-Way Independent Samples Design Advantages and Limitations Comparing Two Groups Comparing t Test to ANOVA Independent Samples t Test Independent Samples ANOVA Comparing
More informationUnconscious Bias: From Awareness to Action!
Unconscious Bias: From Awareness to Action! Did you know that we all have unconscious bias, and that we each have a responsibility to prevent its unintended consequences? In this course, we will explore
More informationA Brief (very brief) Overview of Biostatistics. Jody Kreiman, PhD Bureau of Glottal Affairs
A Brief (very brief) Overview of Biostatistics Jody Kreiman, PhD Bureau of Glottal Affairs What We ll Cover Fundamentals of measurement Parametric versus nonparametric tests Descriptive versus inferential
More informationA Guide to Help You Reduce and Stop Using Tobacco
Let s Talk Tobacco A Guide to Help You Reduce and Stop Using Tobacco Congratulations for taking this first step towards a healthier you! 1-866-710-QUIT (7848) albertaquits.ca It can be hard to stop using
More informationInferential Statistics
Inferential Statistics and t - tests ScWk 242 Session 9 Slides Inferential Statistics Ø Inferential statistics are used to test hypotheses about the relationship between the independent and the dependent
More informationJumpstart Mplus 5. Data that are skewed, incomplete or categorical. Arielle Bonneville-Roussy Dr Gabriela Roman
Jumpstart Mplus 5. Data that are skewed, incomplete or categorical Arielle Bonneville-Roussy Dr Gabriela Roman Questions How do I deal with missing values? How do I deal with non normal data? How do I
More informationCitizens Jury Questionnaire Results
Citizens Jury Questionnaire Results Jury 1 Prepared by Dr Sarah Clement ABSTRACT On 14-16 and 21-23 January 2016, two three-day citizens juries took place in Manchester, tackling policy questions related
More informationAP Statistics Exam Review: Strand 2: Sampling and Experimentation Date:
AP Statistics NAME: Exam Review: Strand 2: Sampling and Experimentation Date: Block: II. Sampling and Experimentation: Planning and conducting a study (10%-15%) Data must be collected according to a well-developed
More informationPractical Statistical Reasoning in Clinical Trials
Seminar Series to Health Scientists on Statistical Concepts 2011-2012 Practical Statistical Reasoning in Clinical Trials Paul Wakim, PhD Center for the National Institute on Drug Abuse 10 January 2012
More informationLiving well today...32 Hope for tomorrow...32
managing diabetes managing managing managing managing managing managing diabetes Scientific research continually increases our knowledge of diabetes and the tools to treat it. This chapter describes what
More informationTHE PUBLIC AND GENETIC EDITING, TESTING, AND THERAPY
THE PUBLIC AND GENETIC EDITING, TESTING, AND THERAPY January 2016 0 INTRODUCTION A new poll of adults in the United States conducted by STAT and Harvard T.H. Chan School of Public Health shows that Americans
More informationThe Relationship between YouTube Interaction, Depression, and Social Anxiety. By Meredith Johnson
The Relationship between YouTube Interaction, Depression, and Social Anxiety By Meredith Johnson Introduction The media I would like to research is YouTube with the effects of social anxiety and depression.
More informationWhy we get hungry: Module 1, Part 1: Full report
Why we get hungry: Module 1, Part 1: Full report Print PDF Does Anyone Understand Hunger? Hunger is not simply a signal that your stomach is out of food. It s not simply a time when your body can switch
More informationProbability, Statistics, Error Analysis and Risk Assessment. 30-Oct-2014 PHYS 192 Lecture 8 1
Probability, Statistics, Error Analysis and Risk Assessment 30-Oct-2014 PHYS 192 Lecture 8 1 An example online poll Rate my professor link What can we learn from this poll? 30-Oct-2014 PHYS 192 Lecture
More informationUNIT I SAMPLING AND EXPERIMENTATION: PLANNING AND CONDUCTING A STUDY (Chapter 4)
UNIT I SAMPLING AND EXPERIMENTATION: PLANNING AND CONDUCTING A STUDY (Chapter 4) A DATA COLLECTION (Overview) When researchers want to make conclusions/inferences about an entire population, they often
More informationSampling Controlled experiments Summary. Study design. Patrick Breheny. January 22. Patrick Breheny Introduction to Biostatistics (BIOS 4120) 1/34
Sampling Study design Patrick Breheny January 22 Patrick Breheny to Biostatistics (BIOS 4120) 1/34 Sampling Sampling in the ideal world The 1936 Presidential Election Pharmaceutical trials and children
More informationHelping the smoker decide to quit
Helping others quit Helping others quit It s difficult to watch someone you care about smoke their lives away. However, smokers need to make the decision to quit because they realise it will benefit them,
More informationTRITON DTS SPINAL DECOMPRESSION
TRITON DTS SPINAL DECOMPRESSION My name is Dr. Will Vargo DC. I m a Back and Neck Pain Specialist certified in Spinal Decompression. My practice is fully dedicated to helping people with exactly the same,
More informationInsomnia: What Happens When You Can't Sleep By Donnee Patrese
Insomnia: What Happens When You Can't Sleep By Donnee Patrese If you are looking for a ebook Insomnia: What happens when you can't sleep by Donnee Patrese in pdf form, then you have come on to the right
More informationExperimental Methods. Policy Track
Experimental Methods Policy Track East Asia Regional Impact Evaluation Workshop Seoul, South Korea Nazmul Chaudhury, World Bank Reference Spanish Version & French Version also available, Portuguese soon.
More informationIntroduction; Study design
; Study design Patrick Breheny January 12 Patrick Breheny STA 580: Biostatistics I 1/43 What is statistics? What is biostatistics, and why is it important? The statistical framework Statistics is the science
More informationMacro tracking 101. But before we get into all that the big question: What is a macro?
Macro tracking 101 The information contained in this document is for informational and entertainment purposes only. If you have questions regarding nutrition, diet and health please consult your physician
More informationLec 02: Estimation & Hypothesis Testing in Animal Ecology
Lec 02: Estimation & Hypothesis Testing in Animal Ecology Parameter Estimation from Samples Samples We typically observe systems incompletely, i.e., we sample according to a designed protocol. We then
More informationTeresa Anderson-Harper
Teresa Anderson-Harper Teresa was nominated as a Reunification Month Hero by a parent attorney who has seen her grow from a parent in a series of dependency cases to the first-ever Family Recovery Support
More informationDangerous States of Mind What To Do When Workers Are Frustrated, Fatigued, Complacent or Hurried
Dangerous States of Mind What To Do When Workers Are Frustrated, Fatigued, Complacent or Hurried By Cristian Sylvestre Principal, SafeTrain Pty Ltd ABSTRACT The paper outlines the findings of research
More informationThe Cinderella Hypothesis
The Hypothesis The Cinderella Hypothesis The Unique Opportunity that s Unfolding for Trigger Point Specialists For people who think they know all about trigger points There s something 90% of people have
More informationTechnical Track Session IV Instrumental Variables
Impact Evaluation Technical Track Session IV Instrumental Variables Christel Vermeersch Beijing, China, 2009 Human Development Human Network Development Network Middle East and North Africa Region World
More informationYou can t fix by analysis what you bungled by design. Fancy analysis can t fix a poorly designed study.
You can t fix by analysis what you bungled by design. Light, Singer and Willett Or, not as catchy but perhaps more accurate: Fancy analysis can t fix a poorly designed study. Producing Data The Role of
More informationHealth Care Callback Survey Topline August 2001
Health Care Callback Survey Topline 8.28.2001 August 2001 Princeton Survey Research Associates for the Pew Internet & American Life Project Sample: n = 500 Internet users who go online for health care
More informationREVIEW PROBLEMS FOR FIRST EXAM
M358K Sp 6 REVIEW PROBLEMS FOR FIRST EXAM Please Note: This review sheet is not intended to tell you what will or what will not be on the exam. However, most of these problems have appeared on or are very
More information1. To review research methods and the principles of experimental design that are typically used in an experiment.
Your Name: Section: 36-201 INTRODUCTION TO STATISTICAL REASONING Computer Lab Exercise Lab #7 (there was no Lab #6) Treatment for Depression: A Randomized Controlled Clinical Trial Objectives: 1. To review
More informationA Strategy for Handling Missing Data in the Longitudinal Study of Young People in England (LSYPE)
Research Report DCSF-RW086 A Strategy for Handling Missing Data in the Longitudinal Study of Young People in England (LSYPE) Andrea Piesse and Graham Kalton Westat Research Report No DCSF-RW086 A Strategy
More informationMeasures of validity. The first positive rapid influenza test in the middle FROM DATA TO DECISIONS
FROM DATA TO DECISIONS Measures of validity BY DANIEL BRONSON-LOWE, PhD, CIC, FAPIC, AND CHRISTINA BRONSON-LOWE, PhD, CCC-SLP, CLD @ISTOCK.COM/OEZ WELCOME TO THE eighth installment in a series examining
More informationPLACEMENT & DIAGNOSTIC TEST
Bitte füllen Sie die Lücken nach bestem Wissen ein, der Text dient lediglich dazu, Sie in eine passende Klasse einzuteilen. PLACEMENT & DIAGNOSTIC TEST Grammar 1. I from France. a) is b) are c) am d) be
More informationHappy Healthy Teeth - A Story About Leo The Lion. Grades: preschool to 1
Happy Healthy Teeth - A Story About Leo The Lion Grades: preschool to 1 Why do we need our teeth? Eating: To chew our food. That s an easy one! Can you imagine trying to eat an apple, carrot or a hamburger
More informationConsider the following aspects of human intelligence: consciousness, memory, abstract reasoning
All life is nucleic acid. The rest is commentary. Isaac Asimov Consider the following aspects of human intelligence: consciousness, memory, abstract reasoning and emotion. Discuss the relative difficulty
More informationHard Edges Scotland: Lived Experience Reference Group
Hard Edges Scotland: Lived Experience Reference Group May 2017 1. Lived Experience Reference Group: Role and Membership 1.1 The Lived Experience Reference Group was established as a core part of the Hard
More informationEinstufungsbogen. Beginner. 1. I from Italy. a) is b) are c) am d) be. 2. This is my friend. name is Peter. a) Her b) Our c) Yours d) His. 3. Paul is.
Einstufungsbogen 1. I from Italy. Beginner a) is b) are c) am d) be 2. This is my friend. name is Peter. a) Her b) Our c) Yours d) His 3. Paul is. a) my sister s friend. b) friend my sister c) friend from
More informationPrueba de nivel. 1. I from France. a) is b) are c) am d) be. 2. This is my friend. name is Peter. a) Her b) Our c) Yours d) His
Prueba de nivel 1. I from France. a) is b) are c) am d) be 2. This is my friend. name is Peter. a) Her b) Our c) Yours d) His 3. Mike is. a) my sister s friend b) friend my sister c) friend from my sister
More informationQuality Digest Daily, March 3, 2014 Manuscript 266. Statistics and SPC. Two things sharing a common name can still be different. Donald J.
Quality Digest Daily, March 3, 2014 Manuscript 266 Statistics and SPC Two things sharing a common name can still be different Donald J. Wheeler Students typically encounter many obstacles while learning
More informationClicker quiz: Should the cocaine trade be legalized? (either answer will tell us if you are here or not) 1. yes 2. no
Clicker quiz: Should the cocaine trade be legalized? (either answer will tell us if you are here or not) 1. yes 2. no Economic Liberalism Summary: Assumptions: self-interest, rationality, individual freedom
More informationPsychology Research Process
Psychology Research Process Logical Processes Induction Observation/Association/Using Correlation Trying to assess, through observation of a large group/sample, what is associated with what? Examples:
More informationINTRODUCTION TO MACHINE LEARNING. Decision tree learning
INTRODUCTION TO MACHINE LEARNING Decision tree learning Task of classification Automatically assign class to observations with features Observation: vector of features, with a class Automatically assign
More informationAn Empirical Study of Nonresponse Adjustment Methods for the Survey of Doctorate Recipients Wilson Blvd., Suite 965, Arlington, VA 22230
An Empirical Study of Nonresponse Adjustment Methods for the Survey of Doctorate Recipients 1 Fan Zhang 1 and Stephen Cohen 1 Donsig Jang 2, Amang Suasih 2, and Sonya Vartivarian 2 1 National Science Foundation,
More informationControlling Worries and Habits
THINK GOOD FEEL GOOD Controlling Worries and Habits We often have obsessional thoughts that go round and round in our heads. Sometimes these thoughts keep happening and are about worrying things like germs,
More informationGood Communication Starts at Home
Good Communication Starts at Home It is important to remember the primary and most valuable thing you can do for your deaf or hard of hearing baby at home is to communicate at every available opportunity,
More informationIncrease your Rapport skills in Health and Well-being, Business and Relationships
Increase your Rapport skills in Health and Well-being, Business and Relationships by Nadine K. Cooper Present State Desired State RESOURCES What you do speaks louder than what you say! NLP Individual Ecology
More informationTutorial 3: MANOVA. Pekka Malo 30E00500 Quantitative Empirical Research Spring 2016
Tutorial 3: Pekka Malo 30E00500 Quantitative Empirical Research Spring 2016 Step 1: Research design Adequacy of sample size Choice of dependent variables Choice of independent variables (treatment effects)
More informationAARP/American Speech-Language-Hearing Association (ASHA)
AARP/American Speech-Language-Hearing Association (ASHA) National Poll on Hearing Health Results Summary John Geraci (jgeraci@cruxresearch.com) The right information for the right decisions. Crux Research,
More informationUSEFUL RESPONSES TO ROUTINE ENQUIRY DOUBTERS
1 USEFUL RESPONSES TO ROUTINE ENQUIRY DOUBTERS (Adapted from DH Mental Health Policy Research Unit publication 2006) Purpose of this paper Sometimes it is frustrating dealing with doubting or reluctant
More informationAn introduction to organ and tissue donation
Student resource Lesson 1: An introduction to organ and tissue donation Lesson outcomes To be able to give a definition of organ and tissue donation, a transplant, a recipient and the NHS Organ Donor Register
More informationStudy Design. Study design. Patrick Breheny. January 23. Patrick Breheny Introduction to Biostatistics (171:161) 1/34
Study design Patrick Breheny January 23 Patrick Breheny Introduction to Biostatistics (171:161) 1/34 in the ideal world In an ideal world, We have a list of everyone in the population of interest We randomly
More informationStress is different for everyone While what happens in the brain and the body is the same for all of us, the precipitating factors are very
1 Stress is different for everyone While what happens in the brain and the body is the same for all of us, the precipitating factors are very individual. What one person experiences as stressful might
More information10/4/2007 MATH 171 Name: Dr. Lunsford Test Points Possible
Pledge: 10/4/2007 MATH 171 Name: Dr. Lunsford Test 1 100 Points Possible I. Short Answer and Multiple Choice. (36 points total) 1. Circle all of the items below that are measures of center of a distribution:
More informationTip sheet. A quick guide to the dos and don ts of mental health care and inclusion. 1. Ask questions. Practical tips
A quick guide to the dos and don ts of mental health care and inclusion Much of the rejection felt by those in church with mental health problems comes from accidental actions and words, delivered with
More information